Estimates of disease burden attributed to particulate matter in northern part of Thailand

During the dry season, the most severe haze problems happen in northern Thailand, and atmospheric PM10 and PM2.5 concentrations during this period exceeded national ambient air quality standards. A potential health risk to the local population was also exacerbated by elevated PM10 and PM2.5 concentrations. Throughout the study years of 2012 to 2016, the total quantity of PM10 emissions from all biomass types, including forests, savannas and grasslands, and agriculture, were 298,002, 279,476, 333,167, 308,540, and 415,173 tons, respectively and that from PM2.5 were 218,283, 204,018, 242,502, 224,720, and 301,494 tons, respectively. Forests generated the majority of PM10 emissions (88.1–91.8%), followed by savannas and grasslands (7.5–11.2%) and agricultural (0.7–1.0%). In consideration with others arthrosporic emissions, biomass burning contributed to around 88% of total emissions in the nine provinces of upper northern Thailand. In accordance with the correlation between measured PM10, PM2.5 and MAIAC-AOD, a linear regression model was developed to predict the PM10, PM2.5 concentration in the area without monitoring station. However, because PM2.5 monitoring was not available in any other studied province during the study period, the prediction of PM2.5 concentration was only made for Chiang Mai. The most MAIAC-AOD was retrieved during the summer, between February and May, at a ratio of 62.5%, followed by the winter (34.8%) and the rainy season (2.7%). The province with higher predicted PM10 concentration showed higher DALYs. Chiang Mai had the greatest DALYs of COPD in 2014, with 4,359 years per 100,000 population, under the highest predicted PM10 concentration of 66 µg/m3. Whereas Lamphun had the greatest DALYs for acute bronchitis, at 4,782 years per 100,000 population, which was associated with the highest PM10 concentration of 65 µg/m3 during in 2015. During 2014-2016, the DALYs of COPD attributed to PM2.5 were 2,589, 2922, and 3,567, respectively, and the DALYs of lung cancer attributed to PM2.5 were 576, 809, and 843, respectively. In conclusion, the ability of MAIAC-AOD to predict the PM10 and PM2.5 concentration at a 1-km resolution could be helpful in managing air pollution control, monitoring, and assessment of the health risks attributed to particulate matter.